Geo-Demographic and Socioeconomic Determinants of Diagnosed Hypertension among Urban Dwellers in Ibadan, Nigeria: A Community-based Study

Background: The relationship between diagnosed high blood pressure (HBP) and proximity to health facilities and noise sources is poorly understood. We investigated the relationship between proximity to noise sources, sociodemographic and economic factors, and diagnosed HBP in Ibadan, Nigeria. Methods: We investigated 13,531 adults from the African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES) study in Ibadan. Using a Geographic Information System (GIS), the locations of healthcare facilities, pharmaceutical shops, bus stops, churches, and mosques were buffered at 100m intervals, and coordinates of persons diagnosed with HBP were overlaid on the buffered features. The number of persons with diagnosed HBP living at every 100m interval was estimated. Gender, occupation, marital status, educational status, type of housing, age, and income were used as predictor variables. Analysis was conducted using Spearman rank correlation and binary logistic regression at p<0.05. Results: There was a significant inverse relationship between the number of persons diagnosed with HBP and distance from pharmaceutical shops (r=−0.818), churches (r=−0.818), mosques (r=−0.893) and major roads (r=−0.667). The odds of diagnosed HBP were higher among the unemployed (AOR=1.58, 95% CI: 1.11–2.24), currently married (AOR=1.45, CI: 1.11–1.89), and previously married (1.75, CI: 1.29–2.38). The odds of diagnosed HBP increased with educational level and age group. Conclusion: Proximity to noise sources, being unemployed and educational level were associated with diagnosed HBP. Reduction in noise generation, transmission, and exposure could reduce the burden of hypertension in urban settings.


Introduction
High blood pressure (HBP) is de ned as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mm Hg. 1 HBP affects more than 1 billion people worldwide, the majority of whom live in low-and middle-income countries. 2Hypertension, high blood pressure (HBP) is the most important risk factor for death and cardiovascular diseases. 3Long exposure to noise > 90 dBA has been identi ed as one of the drivers of HBP. 4,5 Europe, tra c noise is responsible for 1.7 million cases of hypertension. 6Long-term or short-term effects of exposure to environmental noise may affect the autonomic and endocrine systems resulting in changes in BP regulation that then contribute to an increased risk of cardiovascular diseases. 5A meta-analysis of 24 studies revealed that road tra c noise is associated with an elevated risk of HBP. 7 Tra c-related noise at night causes fragmentation of sleep, the elevation of stress hormone levels, and oxidative stress which ultimately promote the development of vascular (endothelial) dysfunction, and HBP. 5 Previous studies on the impacts of noise on CVD have focused on industrial exposure with limited attention to noise from religious organizations. 8In a study of 200 mosques in Riyadh there was signi cantly greater noise outside, compared to inside the prayer rooms.Similarly, noise levels from places of worship were higher than those set by Jordanian limits during day and night time. 9The relationship between HBP and distance from roads and religious centres (churches and mosques), which are noise sources, has not been explored, in Nigeria, where noise from religious centres and other public facilities is unregulated.Therefore, we investigated the relationship between proximity to noise sources and the likelihood of being diagnosed with HBP in a largely indigenous population in Ibadan, Nigeria.In addition, we also examined the relationship between the number of persons diagnosed with HBP and the distance to potential BP monitoring facilities (healthcare facilities and pharmaceutical shops).

Data Collection
Door-to-door survey was used to collect data between 11 October 2021 and 29 August 2022 during the baseline phase of the African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES) project. 10A very highresolution satellite image (Google Earth) showing all the buildings in the demographic surveillance sites (DSS) was converted to PDF format and loaded to Avenza Maps software (https://www.avenza.com/avenza-maps/)for ease of ground navigation to respective buildings.This ensured near-total case inclusion and accurate denominator population.Enumerators interviewed household head or his/her proxy and collected data on demographic characteristics, stroke knowledge, stroke status, ownership of household assets and information on diagnosis of stroke high blood pressure and other related co-morbidity.Data was stored electronically using REDCap mobile app. 11Diagnosed HBP was de ned as previous diagnosis of high blood pressure (BP ≥ 140/90 by a trained healthcare worker using standard protocol and/or current treatment for HBP).Furthermore, data on the locations of healthcare facilities, pharmaceutical shops, churches, mosques, and bus stops were digitized from Google Maps and validated during the data collection survey.

Data Analysis
The urban DSS data were ltered to select only those that had previously been diagnosed with HBP.The geographic coordinates of all these persons were plotted in ArcGIS software.Similarly, the locations of pharmaceutical shops, healthcare facilities, churches, mosques, and bus stops were also plotted and overlaid on the locations of the persons diagnosed with HBP to assess the relationship between distance from these healthcare facilities and noise-generating centres.
3][14] The Rn is calculated as the observed average distance divided by the expected average distance. 12If the average distance is less than the average for a hypothetical random distribution, the distribution of the features is considered clustered, however, if it is greater than a hypothetical random distribution, it is considered dispersed, while if it is 1, it is regarded as random. 14The hotspot of persons diagnosed with HBP was analysed using Kernel Density Estimation (KDE) 15 .The method identi ed localities with higher concentrations of persons diagnosed with HBP. 12 A total of 1,510 persons previously diagnosed with HBP based on their response in the questionnaire were selected from 13,531 respondents.The coordinates of diagnosed HBP patients were overlaid on the road network, the location of churches, mosques, health facilities, and pharmaceutical shops.The number of persons diagnosed with HBP at every 100 meters from each healthcare facility types and noise-generating centres types were enumerated using series of non-overlapping 100 meters multiple ring buffers. 16ArcGIS software was used for all the spatial analyses, while Spearman rank Correlation was used to assess the statistical relationship between distance from healthcare facilities, noise-generating facilities and the number of persons diagnosed with HBP.Binary logistic regression was used to assess the association between the demographic, socioeconomic, and housing variables and the likelihood of HBP diagnosis among the entire sample of 13531 respondents at ρ < 0.05.The dependent variable is a binary categorical variable indicating whether a person reported to have been diagnosed with HBP or otherwise.The regression results were presented as odds ratios (OR) together with their 95% con dence intervals (CIs).All comparisons were considered to be statistically signi cant at p < 0.05 level.In all, 1510 (11.16%) of the 13531 respondents in the urban DSS (Fig. 1) had been diagnosed with HBP.The spatial distribution analysis reveals distinct patterns in the locations of individuals diagnosed with HBP and pharmaceutical shops.The locations of individuals diagnosed with HBP and the location pharmaceutical shops are clustered in the urban DSS (Table 1). 14Conversely, the distribution of hospitals/Clinics, churches, and bus stops are dispersed.However, mosques are randomly distributed (Table 1).(Fig. 2).

Proximity to Healthcare Facilities and Pharmaceutical Shops and Diagnosed HBP
The average distance to healthcare facilities among persons with diagnosed HBP was signi cantly higher (214.35 ± 91.13m) than for those without diagnosed HBP (205.44 ± 94.27m F (1,13464) = 12.037, p = 0.001).However, there was no signi cant relationship between the number of persons diagnosed with HBP and distance from healthcare facilities (r = 0.300, p = 0.312).Thus, while the average distance travelled to hospitals varied between those diagnosed and those not diagnosed with HBP, nonetheless, there was no signi cant relationship between the number of persons diagnosed with HBP and distance from healthcare facilities.The average distance to pharmaceutical shops for persons diagnosed with HBP was slightly but not signi cantly lower (737.11± 455.30m) than for those without diagnosed HBP (740.85 ± 479.06m; F (1,13464) = 0.082, p = 0.774).However, there was a signi cant negative relationship between distance from pharmaceutical shops and the number of persons with diagnosed HBP (r = 0-.818,p = 0.001).Furthermore, there was no signi cant difference in proportions of males (2.7%) with diagnosed HBP, compared to females (8.5%) with diagnosed HBP (χ 2 = 2.085, p = 0.079).Greater percentage (Table 2) of married persons were diagnosed with HBP.Marital status was signi cantly associated with diagnosed HBP (χ 2 = 308.067,df = 3, ρ < 0.05).

Proximity to Churches and Mosques and Diagnosed HBP
Education was also found to be signi cantly associated with diagnosed HBP (χ 2 = 164.773,p < 0.05), with persons who had no formal education exhibiting the highest percentage and those with primary education exhibiting the lowest percentage (Table 2).There is a signi cant occupational difference between those with diagnosed HBP (χ 2 = 107.841,ρ < 0.05) and those without, with professionals having the lowest percentage of persons diagnosed with HBP (Table 4).Regarding health-related variables, diabetes mellitus was signi cantly associated with diagnosed HBP (χ 2 = 181.342,p < 0.05), with individuals without diabetes mellitus being less likely to have diagnosed HBP.Those with diagnosed HBP had a signi cantly higher proportion of high cholesterol than those not diagnosed with HBP (χ 2 = 38.898,p < 0.0001) or obesity (χ 2 = 20.801,p < 0.0001).There was a strong association between stroke and number of diagnosed HBP (χ 2 = 281.253,p < 0.0001), with a greater proportion of respondents who had a stroke also previously diagnosed with HBP (Table 2).Furthermore, tobacco use was signi cantly associated with diagnosed ρ < 0.05I).Alcohol consumption was signi cantly associated with diagnosed HBP (χ 2 = 131.259,p = 0.0001; Table 2).

Socioeconomic Determinants of Diagnosed HBP
The determinants of diagnosed HBP include occupation, marital status, education, housing type, and age.Following adjustment for demographic and socio-economic characteristics, the odds of diagnosed HBP were 58% higher among unemployed persons compared to professionals (AOR = 1.58,CI: 1.11, 2.24).Similarly, the currently married (AOR = 1.45,CI: 1.11, 1.89) and previously married persons (AOR = 1.75, CI: 1.29, 2.38) were more likely to be diagnosed with HBP.The likelihood of HBP diagnosis was highest among those with tertiary education (AOR = 1.64,CI: 1.25, 2.16) relative to those with no formal education.Furthermore, the odds of diagnosed HBP increased with the age of participants (Table 3).The Binary Logistic Regression model correctly classi ed 88.8% of the cases.

Discussion
This study sought to examined the association between the location of persons with diagnosed HBP and location of healthcare facilities (pharmaceutical shops, hospitals/clinic) and noise generating facilities (church, mosques and bus stops).Also, we investigated the socioeconomic predictors of diagnosed HBP among the residents.We established that persons with diagnosed HBP were clustered in space.Consequently, some local factors may be responsible for the observed distributional pattern.An overlay of the locations of churches and mosques on the Kernel Density Estimation Map shows that hotspots of persons with diagnosed HBP are within the localities where all the churches and mosques are located.Similarly, the hotspots of persons with diagnosed HBP also aligned with the major roads.Hence, there is a close association between the distribution of persons with diagnosed HBP and the distribution of churches, mosques and major road alignments.However, a relatively disproportional lower number of the pharmaceutical shops are located within the hotspots of persons diagnosed with HBP.
Access to healthcare is constrained by numerous geographically varying factors, including long travel times to healthcare facilities and poor transportation infrastructure. 17Most of the hospitals/clinics in the area are primary healthcare facilities that respond to the basic healthcare needs of the people.This perhaps accounts for why there was no signi cant relationship between distance from hospitals/clinics and the number of persons with diagnosed HBP.However, the distribution of pharmaceutical shops was clustered because they are often located in response to population and this could explain the negative association between distance from pharmaceutical shops and the number of persons with diagnosed HBP.In addition, proximity to pharmaceutical shops could also serve as an impetus to check one's BP.This is because most of the pharmaceutical shops provide a BP checking service for a small fee to local people.
Persons with diagnosed HBP lived much closer to churches than those without diagnosed HBP.Churches are dispersed and the percentage of persons with diagnosed HBP declined with increasing distance from churches, hence, proximity to churches is associated with diagnosed HBP.Although there is no signi cant difference in proximity to mosques and diagnosed HBP, perhaps because mosques are randomly distributed not necessarily in response to population distribution.Thus, the observed association between the distribution of mosques and HBP is widespread in the study.
There was no association between distance from bus stops and the number of persons with HBP.There was no difference in distance to bus terminals between persons with HBP and those without it.All respondents are within 100 meters distance from non-major roads, which may imply that the neighbourhoods are walkable.Neighbourhoods with high walkability may ameliorate the risk of hypertension and this could play a signi cant role in improving population health. 18However, persons with HBP live at a greater distance away from major roads (dual carriageway) compared with those who do have HBP and the number of persons with HBP decreases with increasing distance from major roads.There is an association between road infrastructure and HBP.Exposure to ambient PM 2.5 from bus terminals can increase BP within a period of a few days while long-term exposure might also promote the development of chronic hypertension. 19There is an association between the level of tra c noise and increased blood pressure among cab drivers. 20e random and dispersed patterns of churches and mosques respectively alluded to the lack of regulations guiding the establishment and approval of locations used by faith-based organizations in the study area and this lacuna exposed the majority of the population to noise pollution from these places of worship.Although churches and mosques have different distributional patterns, a greater number of people diagnosed with HBP live in their proximity.The random distributional pattern of mosques is particularly interesting because it shows that mosques are everywhere.Bus stops are dispersed because they are located in proximity to the road network.Therefore, both intermittent and continuous exposure to noise from religious centres and road networks can exacerbate the incidence of HBP among residents and this may worsen with the increasing density of religious centres in most African countries.
This study showed a strong, inverse relationship between distance to pharmaceutical shops, churches, mosques, road networks and the number of persons with HBP.Apart from noise from roadways, higher near-roadway particulate matter (PM 2.5 ) and black carbon (BC) exposures have been associated with elevated BP. 21Although, primary care clinics are frequently placed in neighbourhoods to improve access. 22It is possible to infer that these facilities were not located in consonance with population distribution but rather based on available space.This further ampli ed the inadequate health infrastructural planning in most developing countries.The absence of a relationship could also be due to the combination of private and public healthcare facilities in the analysis.A similar study noted that the proximity of the home to hospitals did not correlate with primary immunization completion or BP in either a hospital-based or a community clinic. 22ise from haphazardly distributed faith-based centres in the unplanned metropolitan area of Ibadan could predispose residents to HBP. noise levels are seldom studied at non-workplace and non-abode sites that are visited regularly, e.g., places of worship. 8Therefore, ambient noise levels emanating from religious activities in residential neighbourhoods are an emerging environmental problem that has attracted little attention from enforcement agencies and policymakers and components of such religious noise include, but are not limited to, mosque calls, ringing of church bells, megaphones, clapping of hands, loud prayers, chanting, singing, mobile preaching, open-air crusade, night vigils and drumming. 23Noise measurements outside some mosques in Saudi Arabia were higher than 85 dB which is the sound level at which noise-induced hearing loss (NIHL) has been shown to occur in occupational settings. 8Noise from religious centres has been recognized as a major source of sleep disturbances and may become unbearable with the increasing density of religious centres (churches and mosques) in the coming years. 24Apart from sleep disorders, the literature has established the link between noise exposure and HBP.

25-27
The importance of the relationship between socioeconomic and housing variables to HBP was further established in this study.Comparatively, the likelihood of having HBP was higher among the unemployed, married, and divorced persons, while the likelihood of HBP increased with education, age group and income.Current attempts at addressing the persistently high number of persons with HBP have emphasised the need to improve the lifestyle of people with limited attempts at addressing their housing and environmental conditions. 28The ndings that single individuals fare better in terms of their BP compared to their married counterparts complement and build upon earlier research and corroborate other research ndings in this regard.Research has linked marital con ict to heart rate and HBP in laboratory studies. 29Nevertheless, there are pockets of studies where never married persons had a higher risk of HBP when compared to married persons, even after adjustment for different demographic, socioeconomic and lifestyle variables. 30Research also suggests that occupational characteristics contribute to adverse health outcomes such as CVD. 31 Persons of high occupational status are known to have higher BP, especially men. 32High psychological demand at the workplace, may contribute to disease risk, although, having control over one's schedule and one's work may mitigate the pathogenic effects. 33In contrast, 34 found a link between unemployment and worsening health conditions.Unemployment, increasing age, and low income have also been identi ed as cogent risk factors for HBP in this population and this has been reported in other studies. 35clarations Figures The Study Administrative Wards within the Ibadan Metropolitan Area, Oyo State.
Pattern of Diagnosed HBP Patients, Health Care Facility and Noise Generating Locations.

Table 1
Distributional Pattern of HBP, Health Care Facilities and Noise Generating Centres The highest concentration of persons diagnosed with HBP are in localities such as Jayode Hospital area, Ile-Aloko Mosque area, The Bride of Christ Apostolic Church, Ayegbami Mosque, Hardex Logistic area, Olounloleru Mosque, The Hope of Heaven Christ Family Church, and Highland Specialist Hospital and Somide Creative Agency, etc.

Table 2
Variations in Blood Pressure by Socioeconomic and Behavioural Characteristics

Table 3
Sociodemographic and Housing Characteristics of Respondents and the Odds of Diagnosed Hypertension